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007			cr nn 008mamaa
008			150903s2009 xxu| o |||| 0|eng d
020			_a9780387772387 _99780387772387
024	7		_a10.1007/b135794 _2doi
035			_avtls000332785
039		9	_a201509030443 _bVLOAD _c201405070502 _dVLOAD _y201402041055 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
100	1		_aCampagnoli, Patrizia. _eautor _9303817
245	1	0	_aDynamic Linear Models with R / _cby Patrizia Campagnoli, Sonia Petrone, Giovanni Petris.
250			_a1.
264		1	_aNew York, NY : _bSpringer New York, _c2009.
300			_axiii, 252 páginas _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aUse R
500			_aSpringer eBooks
505	0		_aIntroduction: basic notions about Bayesian inference -- Dynamic linear models -- Model specification -- Models with unknown parameters -- Sequential Monte Carlo methods.
520			_aState space models have gained tremendous popularity in recent years in as disparate fields as engineering, economics, genetics and ecology. After a detailed introduction to general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. Whenever possible it is shown how to compute estimates and forecasts in closed form; for more complex models, simulation techniques are used. A final chapter covers modern sequential Monte Carlo algorithms. The book illustrates all the fundamental steps needed to use dynamic linear models in practice, using R. Many detailed examples based on real data sets are provided to show how to set up a specific model, estimate its parameters, and use it for forecasting. All the code used in the book is available online. No prior knowledge of Bayesian statistics or time series analysis is required, although familiarity with basic statistics and R is assumed. Giovanni Petris is Associate Professor at the University of Arkansas. He has published many articles on time series analysis, Bayesian methods, and Monte Carlo techniques, and has served on National Science Foundation review panels. He regularly teaches courses on time series analysis at various universities in the US and in Italy. An active participant on the R mailing lists, he has developed and maintains a couple of contributed packages. Sonia Petrone is Associate Professor of Statistics at Bocconi University,Milano. She has published research papers in top journals in the areas of Bayesian inference, Bayesian nonparametrics, and latent variables models. She is interested in Bayesian nonparametric methods for dynamic systems and state space models and is an active member of the International Society of Bayesian Analysis. Patrizia Campagnoli received her PhD in Mathematical Statistics from the University of Pavia in 2002. She was Assistant Professor at the University of Milano-Bicocca and currently works for a financial software company.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aPetrone, Sonia. _eautor _9303818
700	1		_aPetris, Giovanni. _eautor _9303819
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9780387772370
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/b135794 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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